Optimizing Supplier Selection Strategies in the Automotive Manufacturing Industry

Thesis Abstract

Effective supplier selection remains a critical challenge for automotive manufacturing companies seeking to balance cost, quality, reliability, and strategic partnership objectives in a highly competitive and globalized industry. This study addresses the persistent inefficiencies associated with traditional supplier selection processes, which often rely on subjective judgment and limited quantitative criteria, leading to suboptimal supply chain performance and elevated operational risks. The primary aim of this research is to develop an optimized supplier selection framework that enhances decision-making accuracy and operational efficiency within the automotive manufacturing sector. The specific objectives include evaluating current supplier selection practices, identifying key criteria influencing supplier performance, integrating multi-criteria decision analysis techniques, and formulating an improved selection model tailored for automotive manufacturers. The research adopts a mixed-methods approach, combining qualitative and quantitative research paradigms. The qualitative phase involves thematic analysis of semi-structured interviews with twenty supply chain managers from a leading automotive manufacturer operating in Europe to identify core supplier selection challenges and criteria. The quantitative phase employs a survey administered to a stratified random sample of 150 suppliers, selected based on their engagement with the automotive firm over the past three years. Data collection instruments include structured questionnaires incorporating Likert-scale items and ranking questions aligned with identified criteria. The validity and reliability of instruments are ensured through expert validation and Cronbach’s alpha testing, respectively. Data analysis employs descriptive statistics to profile respondents and their responses, while exploratory factor analysis reduces the dimensionality of supplier criteria, identifying core factors influencing supplier performance. Inferential analysis utilizes multiple regression analysis and Analytic Hierarchy Process (AHP) to elucidate the relative importance of selection criteria and to develop an integrated decision-making model. The study further applies multi-criteria decision analysis (MCDA) techniques to weigh and rank potential suppliers based on multiple performance indicators, thus facilitating an objective and transparent selection process. The anticipated findings suggest that integrating quantitative multi-criteria decision tools with traditional qualitative assessments significantly improves the accuracy of supplier evaluation, resulting in better alignment with organizational strategic goals. Key criteria, such as delivery reliability, quality compliance, cost efficiency, and innovation capacity, are expected to emerge as primary determinants in supplier prioritization. The findings are anticipated to demonstrate that companies employing optimized selection frameworks experience reduced procurement costs, enhanced supplier performance, and improved supply chain resilience. This research contributes to existing knowledge by advancing the theoretical understanding of multi-criteria supplier evaluation models robustly tailored for the automotive industry, integrating the Principles of Total Cost of Ownership (TCO) and the Resource-Based View (RBV) with contemporary decision analysis techniques. It also offers practical insights for supply chain managers seeking to refine procurement strategies through evidence-based decision-making tools, thereby bridging the gap between theory and practice. In conclusion, the study advocates for the systematic adoption of integrated multi-criteria decision-making frameworks in supplier selection processes to optimize supply chain performance in the automotive industry. Recommendations include the development of customized supplier evaluation software, capacity building for procurement personnel in decision analysis techniques, and continuous review of supplier criteria to adapt to evolving industry standards. Future research avenues are proposed to validate the framework across different automotive contexts and to explore the impact of emerging technologies such as artificial intelligence and blockchain on supplier evaluation processes. This comprehensive approach aims to reinforce the strategic role of supplier selection as a contributor to competitive advantage and sustainable growth in the automotive manufacturing industry.